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EMAN2 中的新软件工具受 EMDatabank 映射挑战启发。

New software tools in EMAN2 inspired by EMDatabank map challenge.

机构信息

Graduate Program in Quantitative and Computational Biology, Baylor College of Medicine, United States.

Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, United States.

出版信息

J Struct Biol. 2018 Nov;204(2):283-290. doi: 10.1016/j.jsb.2018.09.002. Epub 2018 Sep 4.

Abstract

EMAN2 is an extensible software suite with complete workflows for performing high-resolution single particle analysis, 2-D and 3-D heterogeneity analysis, and subtomogram averaging, among other tasks. Participation in the recent CryoEM Map Challenge sponsored by the EMDatabank led to a number of significant improvements to the single particle analysis process in EMAN2. A new convolutional neural network particle picker was developed, which dramatically improves particle picking accuracy for difficult data sets. A new particle quality metric capable of accurately identifying "bad" particles with a high degree of accuracy, no human input, and a negligible amount of additional computation, has been introduced, and this now serves as a replacement for earlier human-biased methods. The way 3-D single particle reconstructions are filtered has been altered to be more comparable to the filter applied in several other popular software packages, dramatically improving the appearance of sidechains in high-resolution structures. Finally, an option has been added to perform local resolution-based iterative filtration, resulting in local resolution improvements in many maps.

摘要

EMAN2 是一个可扩展的软件套件,具有完整的工作流程,可用于执行高分辨率单颗粒分析、2D 和 3D 异质性分析以及子断层平均等任务。参与最近由 EMDatabank 赞助的 CryoEM Map Challenge,使得 EMAN2 中的单颗粒分析过程有了许多显著的改进。开发了一种新的卷积神经网络颗粒挑选器,极大地提高了困难数据集的颗粒挑选准确性。引入了一种新的颗粒质量指标,能够在无需人工输入和极少额外计算的情况下,高度准确地识别“不良”颗粒,现在它已替代了早期基于人工的方法。3D 单颗粒重建的滤波方式已被改变,以使其更类似于其他几个流行软件包应用的滤波器,极大地改善了高分辨率结构中侧链的外观。最后,添加了一个选项,可执行基于局部分辨率的迭代滤波,从而提高许多映射的局部分辨率。

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